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Pointcept

Perceive the world with sparse points, a codebase for point cloud perception research. Latest works: Concerto (NeurIPS'25), Sonata (CVPR'25 Highlight), PTv3 (CVPR'24 Oral)

PTv3, Sonata, Concerto 등을 통합 지원하는 point cloud perception 연구 프레임워크

Implementations

It is also an official implementation of the following paper:

  • 🚀 Utonia - Toward One Encoder for All Point Clouds
  • Concerto - Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations
  • Sonata - Self-Supervised Learning of Reliable Point Representations
  • Point Transformer V3 - Simpler, Faster, Stronger
  • OA-CNNs - Omni-Adaptive Sparse CNNs for 3D Semantic Segmentation
  • Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training
  • Masked Scene Contrast - A Scalable Framework for Unsupervised 3D Representation Learning
  • Learning Context-aware Classifier for Semantic Segmentation (3D Part)
  • Point Transformer V2 - Grouped Vector Attention and Partition-based Pooling
  • Point Transformer

Additionally, Pointcept integrates the following excellent work (contain above):

  • Backbone: MinkUNet, SpUNet, SPVCNN, OACNNs, PTv1, PTv2, PTv3, StratifiedFormer, OctFormer, Swin3D
  • Semantic Segmentation: Mix3d, CAC
  • Instance Segmentation: PointGroup
  • Pre-training: PointContrast, Contrastive Scene Contexts, Masked Scene Contrast, Point Prompt Training, Sonata, Concerto
  • Datasets: ScanNet, ScanNet200, ScanNet++, S3DIS, ArkitScene, HM3D, Matterport3D, Structured3D, SemanticKITTI, nuScenes, ModelNet40, Waymo

Requirements

  • Ubuntu: 18.04 and above.
  • CUDA: 11.3 and above.
  • PyTorch: 1.10.0 and above.

See also

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